AnthonyHigney
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anthonyhigney.bsky.social
AnthonyHigney
@anthonyhigney.bsky.social
210 followers 1.5K following 17 posts
Economist at Glasgow University. Environmental economics and meta-research.

https://anthonychigney.github.io/home/
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anthonyhigney.bsky.social
Why is there such a difference in loneliness by gender for teenagers? who.int/europe/publica…
The image shows data from a WHO report on adolescent mental health, focusing on loneliness among 11-, 13-, and 15-year-olds across 44 countries. It highlights that 16% of adolescents report feeling lonely most of the time or always, with rates nearly doubling from age 11 to 15. Girls consistently report higher levels than boys. A heatmap table shows the prevalence by age, gender, and country, with darker shades indicating higher rates. Notably high rates among 15-year-old girls include the UK (40%), Belgium (French, 37%), Finland (26%), and Germany (32%), with much lower rates for boys. The report also notes that loneliness is more common among adolescents from low-affluence families.
April 2, 2025 at 6:56 AM
anthonyhigney.bsky.social
Paper finds heat pump adoption in UK led to 70% less carbon usage and 40% less energy.
www.nber.org/202502/diges...
The figure is a line graph titled **"Heat Pump Adoption and Weekly Energy Consumption, Great Britain."** - **Y-axis:** "Change in weekly consumption, kWh," ranging from -350 to 150 in increments of 50. - **X-axis:** "Weeks since adoption," ranging from -50 to 90, with a vertical dashed line at week 0 marking the **"Week of heat pump adoption."** The graph displays two lines with confidence intervals (shaded areas): - **Blue line ("Electricity")**: Starts near zero before adoption, then jumps to approximately +50 kWh after adoption and gradually increases over time. - **Red line ("Gas")**: Starts near zero before adoption, then drops sharply to around -200 kWh after adoption. It shows seasonal variation afterward, with consumption dropping as low as -250 kWh around week 70. **Source:** Researchers' calculations using data from Octopus Energy.
February 3, 2025 at 7:06 PM
anthonyhigney.bsky.social
Reading this paper now. Interesting but I don't quite agree with his take on "true" p-values. Will post about it next week. arxiv.org/abs/1603.07532
We present the expected values from p-value hacking as a choice of the minimum p-value among m independents tests, which can be considerably lower than the "true" p-value, even with a single trial, owing to the extreme skewness of the meta-distribution. We first present an exact probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena. We derive the distribution for small samples 2<n≤n∗≈30 as well as the limiting one as the sample size n becomes large. We also look at the properties of the "power" of a test through the distribution of its inverse for a given p-value and parametrization. The formulas allow the investigation of the stability of the reproduction of results and "p-hacking" and other aspects of meta-analysis. P-values are shown to be extremely skewed and volatile, regardless of the sample size n, and vary greatly across repetitions of exactly same protocols under identical stochastic copies of the phenomenon; such volatility makes the minimum p value diverge significantly from the "true" one. Setting the power is shown to offer little remedy unless sample size is increased markedly or the p-value is lowered by at least one order of magnitude.
December 12, 2024 at 3:21 PM
anthonyhigney.bsky.social
Read the study yourself. t.co/BoS1kMsYlg
Abstract Does lead pollution increase crime? We perform the first meta-analysis of the effect of lead on crime, pooling 542 estimates from 24 studies. The effect of lead is overstated in the literature due to publication bias. Our main estimates of the mean effect sizes are a partial correlation of 0.16, and an elasticity of 0.09. Our estimates suggest the abatement of lead pollution may be responsible for 7–28% of the fall in homicide in the US. Given the historically higher urban lead levels, reduced lead pollution accounted for 6–20% of the convergence in US urban and rural crime rates. Lead increases crime, but does not explain the majority of the fall in crime observed in some countries in the 20th century. Additional explanations are needed.
December 12, 2024 at 12:52 PM
anthonyhigney.bsky.social
They also cut the bottom off that figure.
Funnel plot with elasticity effect size on x axis and precision (1/se) on y. Shows long right tail and smaller left tail at low precision. Clear positive and significant effect towards top of funnel.
December 12, 2024 at 12:52 PM
anthonyhigney.bsky.social
This person is using my study/chart in a misleading way on twitter.
There is a correlation here between the SE and effect size that is picked up by pub bias methods. Partly this is due to underlying study characteristics. Adjust for this and we find there is pub bias, but lead does cause crime.
Screenshot of twitter account Cremieux. Basically says lead has no effect on crime citing my meta-analysis. Inlcudes funnel plot with partial correlations on the x axis and precision (1/se) on y axis). More precise effects centered around zero PCC.
December 12, 2024 at 12:52 PM
anthonyhigney.bsky.social
I also have a brief discussion of how you need to meet the assumption of exchangeability at least in meta-analysis. Happy to hear if I am wrong about anything. Only way to learn.
The authors and others have argued that this shows if effects are heterogeneous, and researchers have a good idea of the effect size ex-ante, and therefore efficiently select sample sizes to all have, for example, 80% power, there will be a correlation between the errors and the effects. This is true but would mean all the studies are estimating effects that are not only known to be different but we know how they are different. We can rank the effects. Should they be doing that? No! In general, in a (random effects) meta-analysis, we assume each study is estimating a different effect and estimate the average of the effects, but the differences are assumed to be random, drawn from a common underlying distribution. When this is not the case, they are not i.i.d, nor exchangeable. What that means is any inference we do from this model will be off. This is important because when we test for publication bias, we are not just describing the data, we are doing inference.
December 6, 2024 at 4:05 PM
anthonyhigney.bsky.social
For example, I think they combine incommensurable effects.
“…[E]ach of these data sets comprises studies on disparate processes, using radically different procedures and designs. Few meta-analysts would be interested in pooling such diverse effects.” In other words, they are combining studies that are looking at completely different things! For me, this is fatal for the empirical part of the paper. This is “combining incommensurable results” and I cannot see any good reason for doing so. For example, they are combining a replication of how much people agree “the earth is flat” with a replication of the effect of less caveolin-1 in mice!
December 6, 2024 at 4:05 PM
anthonyhigney.bsky.social
The National Milk Scheme led to large increases in Milk consumption (Hollingsworth, 1951). Much lower now.
A line graph of milk and milk products from 1940-1050 in th UK. Main point I wish to make is that milk was 383 lb per head before the war and rose to 542 lb per head by 1950. Line graph showing UK per capita milk consumption. Going from 140 L per person in 1974 to about 70 in 2017-18.
November 19, 2024 at 7:56 PM